GAN Inversion. A well-trained GAN usually captures useful statistics and semantics underlying the training data. In the 2D domain, GAN prior has been explored extensively in various image restoration and editing tasks [1]}, [2]}, [3]}. GAN inversion, the common method in this line of work, finds a latent code that best reconstructs the given image using the pre-trained generator.
Typically, the target latent code can be obtained via gradient descent [4]}, [5]}, projected by an additive encoder that learns the inverse mapping of a GAN [6]}, or a combination of them [7]}.
There are recent attempts to apply GAN inversion in the 3D domain. Zhang . [8]} use a pre-trained point cloud GAN to address shape completion in the canonical pose, giving remarkable generalization for out-of-domain data such as real-world partial scans. Pan . [9]} recover the geometric cues from pre-trained 2D GANs and achieve exceptional reconstruction results, but the reconstructed shapes are limited to 2.5D due to limited poses that 2D GANs can synthesize. In this work, we directly exploit the prior from a 3D GAN to reconstruct the shape and texture of complete 3D objects.
